Junqiang Song

CPU/GPU computing for long-wave radiation physics on large GPU clusters

Geoscience simulations rely heavily on high performance computing (HPC) systems. To date, many CPU/GPU heterogeneous HPC systems have been established on which many geoscience simulations have been performed. For most of these simulations on GPU clusters, it can be observed that only the GPUs computational capacity has been exploited to accomplish the arithmetic operations while that of the CPU is ignored, which results in an underutilization of the computing resources within the entire HPC system. In this paper, we perform a long-wave radiation simulation by exploiting the computational capacities of both CPUs and GPUs in the Tianhe-1A supercomputer. First, the long-wave radiation process is accelerated with a Tesla M2050GPU and achieves significant speedup over the baseline performance on a single Intel X5670 CPU core. Second, a workload distribution scheme based on the speedup feedback is proposed and validated with various workloads. Third, a parallel programming model (MPI+OpenMP/CUDA) is presented and utilized when simulating the radiation physics on large GPU clusters. Finally, we address the computational efficiency issue by exploiting the available computing resources within the Tianhe-1A supercomputer. Experimental results demonstrate that the hybrid version can be accomplished within much less time than that of the CPU counterpart; also, they show similar sensitivity to the temporal resolution of the radiation process.

Exploring portfolio scheduling for long-term execution of scientific workloads in IaaS clouds

Long-term execution of scientific applications often leads to dynamic workloads and varying application requirements. When the execution uses resources provisioned from IaaS clouds, and thus consumption-related payment, efficient and online scheduling algorithms must be found. Portfolio scheduling, which selects dynamically a suitable policy from a broad portfolio, may provide a solution to this problem. However, selecting online the right policy from possibly tens of alternatives remains challenging. In this work, we introduce an abstract model to explore this selection problem. Based on the model, we present a comprehensive portfolio scheduler that includes tens of provisioning and allocation policies. We propose an algorithm that can enlarge the chance of selecting the best policy in limited time, possibly online. Through trace-based simulation, we evaluate various aspects of our portfolio scheduler, and find performance improvements from 7% to 100% in comparison with the best constituent policies and high improvement for bursty workloads.

A QSQL-based Efficient Planning Algorithm for Fully-automated Service Composition in Dynamic Service Environments

Web service composition is emerging as a promising technology for supporting large-scale, sophisticated business process integration in a variety of complex e-science or e-business domains. Particularly, semantics have been proposed as a key to automatically solving the discovery and composition problem. However, most of semantic composition approaches still remain at a stage of low efficiency because of the performance issues brought by the involved ontology reasoning and manual processing. To address this problem, in this paper, we present a QSQL-based service composition algorithm towards a fully-automated service composition. QSQL (Quick Service Query List) is an efficient service query index list which can achieve about the same semantic service discovery effects as other existing semantic composition methods, but with much less reasoning. With our proposed QSQL-based service composition algorithm, composition plans can be created to meet a users query in an automatic, efficient and semantic manner. In particular, with our algorithm, most existing composition plans in QSQL can be founded and ranked by exploiting a weighted Petri net representation; which will facilitate the execution verification. The final experiment is conducted to further demonstrate the feasibility of our proposed composition approach and its efficiency.

Spectral methods using Legendre wavelets for nonlinear Klein \ Sine-Gordon equations

Klein/Sine-Gordon equations are very important in that they can accurately model many essential physical phenomena. In this paper, we propose a new spectral method using Legendre wavelets as basis for numerical solution of Klein ? Sine-Gordon Equations. Due to the good properties of wavelets basis, the proposed method can obtain good spatial and spectral resolution. Moreover, the presented method can save more memory and computation time benefit from save more computation time benefit from the hierarchical scale structure of Legendre wavelets. 1D and 2D examples are included to demonstrate the validity and applicability of the new technique. Numerical results show the exponential convergence property and error characteristics of presented method.

A Coupled Method of Laplace Transform and Legendre Wavelets for Lane-Emden-Type Differential Equations

A coupled method of Laplace transform and Legendre wavelets is presented to obtain exact solutions of Lane-Emden-type equations. By employing properties of Laplace transform, a new operator is first introduced and then its Legendre wavelets operational matrix is derived to convert the Lane-Emden equations into a system of algebraic equations. Block pulse functions are used to calculate the Legendre wavelets coefficient matrices of the nonlinear terms. The results show that the proposed method is very effective and easy to implement.

Fractional Variational Iteration Method versus Adomian’s Decomposition Method in Some Fractional Partial Differential Equations

A comparative study is presented about the Adomian’s decomposition method (ADM), variational iteration method (VIM), and fractional variational iteration method (FVIM) in dealing with fractional partial differential equations (FPDEs). The study outlines the significant features of the ADM and FVIM methods. It is found that FVIM is identical to ADM in certain scenarios. Numerical results from three examples demonstrate that FVIM has similar efficiency, convenience, and accuracy like ADM. Moreover, the approximate series are also part of the exact solution while not requiring the evaluation of the Adomian’s polynomials.

Couple of the Variational Iteration Method and Fractional-Order Legendre Functions Method for Fractional Differential Equations

We present a new numerical method to get the approximate solutions of fractional differential equations. A new operational matrix of integration for fractional-order Legendre functions (FLFs) is first derived. Then a modified variational iteration formula which can avoid “noise terms” is constructed. Finally a numerical method based on variational iteration method (VIM) and FLFs is developed for fractional differential equations (FDEs). Block-pulse functions (BPFs) are used to calculate the FLFs coefficient matrices of the nonlinear terms. Five examples are discussed to demonstrate the validity and applicability of the technique.

Couple of the Variational Iteration Method and Legendre Wavelets for Nonlinear Partial Differential Equations

This paper develops a modified variational iteration method coupled with the Legendre wavelets, which can be used for the efficient numerical solution of nonlinear partial differential equations (PDEs). The approximate solutions of PDEs are calculated in the form of a series whose components are computed by applying a recursive relation. Block pulse functions are used to calculate the Legendre wavelets coefficient matrices of the nonlinear terms. The main advantage of the new method is that it can avoid solving the nonlinear algebraic system and symbolic computation. Furthermore, the developed vector-matrix form makes it computationally efficient. The results show that the proposed method is very effective and easy to implement.

A clustering based coscheduling strategy for efficient scientific workflow execution in cloud computing

Due to its advantages of cost‐effectiveness, on‐demand provisioning and easy for sharing, cloud computing has grown in popularity with the research community for deploying scientific applications such as workflows. Although such interests continue growing and scientific workflows are widely deployed in collaborative cloud environments that consist of a number of data centers, there is an urgent need for exploiting strategies which can place application datasets across globally distributed data centers and schedule tasks according to the data layout to reduce both latency and makespan for workflow execution. In this paper, by utilizing dependencies among datasets and tasks, we propose an efficient data and task coscheduling strategy that can place input datasets in a load balance way and meanwhile, group the mostly related datasets and tasks together. Moreover, data staging is used to overlap task execution with data transmission in order to shorten the start time of tasks. We build a simulation environment on Tianhe supercomputer for evaluating the proposed strategy and run simulations by random and realistic workflows. The results demonstrate that the proposed strategy can effectively improve scheduling performance while reducing the total volume of data transfer across data centers. Concurrency and Computation: Practice and Experience, 2013.© 2013 Wiley Periodicals, Inc.

Graph-Cut Based Coscheduling Strategy Towards Efficient Execution of Scientific Workflows in Collaborative Cloud Environments

Recently, cloud computing has emerged as a promising computing infrastructure for performing scientific workflows by providing on-demand resources. Meanwhile, it is convenient for scientific collaboration since different cloud environments used by the researchers are connected through Internet. However, the significant latency arising from frequent access to large datasets and the corresponding data movements across geo-distributed data centers has been an obstacle to hinder the efficient execution of data-intensive scientific workflows. In this paper, we propose a novel graph-cut based data and task co scheduling strategy for minimizing the data transfer across geo-distributed data centers. Specifically, a dependency graph is firstly constructed from workflow provenance and cut into sub graphs according to the datasets which must appear in fixed data centers by a multiway cut algorithm. Then, the sub graphs might be recursively cut into smaller ones by a minimum cut algorithm referring to data correlation rules until all of them can well fit the capacity constraints of the data centers where the fixed location datasets reside. In this way, the datasets and tasks are distributed into target data centers while the total amount of data transfer between them is minimized. Additionally, a runtime scheduling algorithm is exploited to dynamically adjust the data placement during execution to prevent the data centers from overloading. Simulation results demonstrate that the total volume of data transfer across different data centers can be significantly reduced and the cost of performing scientific workflows on the clouds will be accordingly saved.